Citation: Yuan, J.; Liu, Z.; Lian, Y.;
Chen, L.; An, Q.; Wang, L.; Ma, B.
Global Optimization of UAV Area
Coverage Path Planning Based on
Good Point Set and Genetic
Algorithm. Aerospace 2022, 9, 86.
https://doi.org/10.3390/
aerospace9020086
Academic Editor: Kamil Krasuski
Received: 24 December 2021
Accepted: 1 February 2022
Published: 7 February 2022
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Article
Global Optimization of UAV Area Coverage Path Planning
Based on Good Point Set and Genetic Algorithm
Jinbiao Yuan
1
, Zhenbao Liu
1,2,
*, Yeda Lian
1,
*, Lulu Chen
1
, Qiang An
3
, Lina Wang
1
and Bodi Ma
1
1
School of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, China;
yuanjinbiao0414@126.com (J.Y.); chenlulu@mail.nwpu.edu.cn (L.C.); lena@mail.edu.cn (L.W.);
mabodi0112@126.com (B.M.)
2
Research & Development Institute in Shenzhen, School of Civil Aviation, Northwestern Polytechnical
University, Shenzhen 518057, China
3
Research Institute of Aero-Engine, Beihang University, Beijing 100190, China; anqiang@buaa.edu.cn
* Correspondence: liuzhenbao@nwpu.edu.cn (Z.L.); RunnerLian01@163.com (Y.L.)
Abstract:
When performing area coverage tasks in some special scenarios, fixed-wing aircraft con-
ventionally adopt the scan-type of path planning, where the distance between two adjacent tracks is
usually less than the minimum turning radius of the aircraft. This results in increased energy con-
sumption during turning between adjacent tracks, which means a reduced task execution efficiency.
To address this problem, the current paper proposes an area coverage path planning method for a
fixed-wing unmanned aerial vehicle (UAV) based on an improved genetic algorithm. The algorithm
improves the primary population generation of the traditional genetic algorithm, with the help of
better crossover operator and mutation operator for the genetic operation. More specifically, the
good point set algorithm (GPSA) is first used to generate a primary population that has a more
uniform distribution than that of the random algorithm. Then, the heuristic crossover operator and
the random interval inverse mutation operator are employed to reduce the risk of local optimization.
The proposed algorithm is verified in tasks with different numbers of paths. A comparison with the
conventional genetic algorithm (GA) shows that our algorithm can converge to a better solution.
Keywords: UAV; area coverage; GA; path planning; GPSA
1. Introduction
In recent years, unmanned aerial vehicles (UAVs) have been widely used in mapping,
photography, search and rescue, strike, reconnaissance, and mountain, urban, highway,
and oil/gas inspections. As part of path planning, coverage path planning (CPP) [
1
–
3
] of
UAV is also very important in mapping, searching, and patrol inspection [
4
–
7
]. Among
commonly used UAVs, rotor UAVs are flexible, can hover and vertically lift, and have low
requirements on the take-off site, but at the same time, suffer from small load, low cruise
speed, and short endurance time. On the other hand, fixed-wing UAVs are less flexible than
rotorcraft, but they have heavy load, fast cruise speed, and long endurance. Therefore, the
use of rotorcraft has advantages in missions featuring small areas, large terrain fluctuations,
fixed-point lighting, fixed-point shouting, and short-range high-precision tracking shooting,
whereas for large-area and long-distance area coverage, patrol inspection, mapping, and
search, fixed-wing UAVs are obviously more suitable.
Fixed-wing UAVs in area CPP applications usually plan a scan-type path (a group of
parallel lines covering a certain area that are executed in turns, and no crossing of lines
is allowed) based on the actual need. While such demand can be met most of the time,
in some special applications such as digital orthophoto map (DOM) generation and tilt
photography model generation, the lateral distance between two adjacent planned paths
is usually less than the UAV’s turning radius. This increases the distance traveled by the
fixed-wing UAV, leading to additional energy consumption and thereby affecting the UAV’s
Aerospace 2022, 9, 86. https://doi.org/10.3390/aerospace9020086 https://www.mdpi.com/journal/aerospace
